mlsanityData to 7 Oct 2026

Improved Techniques for Training GANs

arXiv 1606.03498 ↗First posted 10 Jun 2016cs.LG cs.CV cs.NEcited by 2,938

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Cited by 820 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework. We focus on two applications of GANs: semi-supervised learning, and the generation of images that humans find visually realistic. Unlike most work on generative models, our primary goal is not to train a model that assigns high likelihood to test data, nor do we require the model to be able to learn well without using any labels. Using our new techniques, we achieve state-of-the-art results in semi-supervised classification on MNIST, CIFAR-10 and SVHN. The generated images are of high quality as confirmed by a visual Turing test: our model generates MNIST samples that humans cannot distinguish from real data, and CIFAR-10 samples that yield a human error rate of 21.3%. We also present ImageNet samples with unprecedented resolution and show that our methods enable the model to learn recognizable features of ImageNet classes.

Key facts. Cited by 2,938 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Jun 2016.

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Citing papers per year: 2018: 3; 2019: 1; 2020: 3; 2021: 488; 2022: 397; 2023: 536; 2024: 553; 2025: 574; 2026: 383.

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  8. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,595
  9. 2022Video Diffusion Modelscited by 2,280
  10. 2023Consistency Modelscited by 1,778
  11. 2023VBench: Comprehensive Benchmark Suite for Video Generative Modelscited by 1,755
  12. 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,665
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  2. 2020DiffWave: A Versatile Diffusion Model for Audio Synthesiscited by 1,339
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  4. 2022Diffusion Models in Vision: A Surveycited by 1,008
  5. 2022CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformerscited by 973
  6. 2023GenEval: An Object-Focused Framework for Evaluating Text-to-Image Alignmentcited by 946
  7. 2023MVDream: Multi-view Diffusion for 3D Generationcited by 928
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  11. 2023Extracting Training Data from Diffusion Modelscited by 723
  12. 2022On Distillation of Guided Diffusion Modelscited by 721
  13. 2022Point-E: A System for Generating 3D Point Clouds from Complex Promptscited by 694
  14. 2021CogView: Mastering Text-to-Image Generation via Transformerscited by 691
  15. 2024Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Thinkcited by 666
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  19. 2021Masked Feature Prediction for Self-Supervised Visual Pre-Trainingcited by 573
  20. 2021Designing an Encoder for StyleGAN Image Manipulationcited by 542
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  24. 2022StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasetscited by 489
  25. 2022Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priorscited by 487
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  27. 2022MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledgecited by 469
  28. 2021MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontierscited by 466
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  35. 2021SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Modelscited by 415
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  37. 2022Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesiscited by 386
  38. 2021GAN Inversion: A Surveycited by 366

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